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Machine Learning Software

What is Machine Learning?

Machine Learning is a branch of artificial intelligence and computer vision. It refers to computer software written to detect patterns in data and images, which then are used to create algorithms that identify, classify, and predict targeted elements.

Once the software is "trained" or has "learned," and it's exposed to new data, it can make decisions independently without explicit programming, and perform new tasks or perform tasks more effectively.

Your data Our algorithms Model Learning Task

Machine Learning applications

If there is relevant data, there is an use for Machine Learning in your business.

 

Marketing and Retail

Using machine learning, we can organize and classify your market and customer data to create predictive models to:

  • Facilitate market segmentation analysis
  • Anticipate customer buying behaviors
  • Reveal traffic where ads will be most effective
  • Plan inventory automatically
  • Segment age and gender using face recognition
  • Determine customer demographics and categorize them into groups
  • Analyze head positioning and posture analysis to better understand client behaviors
  • Assist your customers' purchasing decisions

Manufacturing

Machine Learning will be used to improve factory processes, increase productivity, and create algorithms that can learn and control robotics with minimal human input in what is being touted as the next new workplace revolution: Industry 4.0, also referred to as the "smart factory."

Machine Learning will be used to:

  • Forecast demand
  • Generate virtual inventory or a virtual store
  • Identify and count items on a belt
  • Predict maintenance requirements

Science and Health

Given the current pace of medical research and new technology, the database of medical knowledge is primed for data mining. Understanding it and making it useful is now possible with Machine Learning technology.

Sample Applications:

  • Predict possible pathologies using genetics, family history, and previous illnesses
  • Recommend the right diets, supplements and exercises to patients (pro-active healthcare)
  • Efficiently compare data for research studies
  • Identify and count elements in images or videos
  • Sport motion analysis
  • Posture analysis to improve performance

Finances

Analyzing data from a wide variety of sources makes it possible to predict market behaviors, people's future finances and demand/offer tendencies more accurate.

Sample Applications:

  • Risk assessment and management
  • Algorithmic market trading
  • Portfolio/asset management
  • Financial product recommendations/sales
  • Market tendencies

Energy and Utilities

Sample Applications:

  • Price analysis and trends of raw materials
  • Seismic data processing
  • Utility trading
  • Demand and supply of energy and mining

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